1. Field of Invention
The present invention generally relates to a method for processing tone of a digital color image, and more particularly relates to a method for deciding a semi-S curve for processing tone of a raster digital color image according to the tone scale (e.g., RGB, YCrCb Luv or LCH) of the image.
2. Related Art
In processing digital color image, besides the resolution as a factor directly relating to quality of image, the fidelity of color is the most important factor of all. In comparison to the control of resolution, the control of color is more complicated. The current color systems for defining color mainly include RGB (red, green, blue) primaries, HSB (hue, saturation, brightness) or LCH (lightness, chroma, hue) parameters, YCrCb, Lab, Luv, CIE XYZ color values, and CMYK (cyan, magenta, yellow, black) primaries. No matter what color system is used, human eyes are particularly sensitive to the condition of gray scale. The gray scale in RGB system is specified as tone scale, while gray scales in other color systems are defined by the brightness parameters.
According to research, a digital image can be processed of its tone based on an S-shape curve, as shown in FIG. 2, in order to improve the contrast of brightness, and saturation of colors for an image output device, such as display, printer. In different color systems, the meanings of tone scale are different. For example, in the lightness, chroma, hue modes (YCrCb§, LCH, Lab, Luv), CIE XYZ system or CMYK system, the tone scale is the digital levels (e.g., 256 levels by 8-bit) from white to black, or digital levels of saturation. In the RGB mode, the tone scale is the digital brightness of each primary color (red, green or blue), or the brightness of the specific color. In S curve of FIG. 2, the horizontal axis represents tone scale of original image, while the vertical axis represents tone scale of processed image. In a RGB digital image, for example, the S curve process is to transform the tone scales of the red, green and blue primaries of the original into better tone scales according to the S curve. The original curve, before the process, is a 45-degree linear line shown in FIG. 2. In order to improve the result of brightness contrast and color saturation for an image output device, the tone scale curve has to be changed. A basic and effective method is using histogram equalization, which may achieve best result of contrast and saturation, but will destroy the relationships between contents of the image and makes the image unbalanced. Therefore, the S-shape tone scale process curve is recently used for improving the contrast and saturation of image for an image output device, and still mainly remaining balance of contents of the image.
Generally, an output device, such as color monitor, printer or the like, for digital image can linearly present the brightness and color saturation of the image, or an S curve can be applied for improving the contrast and saturation of the image. But, as shown in FIG. 2, different devices, such as a monitor and a printer, have their different characteristic ranges. Therefore, they need specific S curves. Prior art for the S curve process is to use a specific curve for the specific device.
But, in practice, the tone scales, for example, R, G, B tone scales for RGB digital images, are different from one another for different images. So, a fixed S curve is not suitable for processing all images.
Conventional processes using S curve for transforming tone scale are mainly in three kinds. The first kind is to use a fixed S curve without consideration of histogram of the image. This manner cannot well adjust every different image. The second kind is to provide some different kinds (for example, scenery or people) of images with different S curves. But it still cannot accommodate the specific tone scale of each image. The third kind is to provide an S curve function, such as sine curve or Gaussian function, which can be controlled with an amplitude factor for adjustment of contrast. The third manner can provide a better result. But a single S curve may not be suitable for processing whole image of unbalanced histogram. Further, human eyes will adapt the vision to the image color distribution and decrease the effects of the high occurance color in an image. The affect of human vision to the high occurance color is not considered by the prior art processes.
Therefore, we need a tone scale process method that provides a semi-S curve tone process and a weighting function for high occurance color to solve the problem.